The present exemplary embodiments pertain to digital shelves and, more particularly, to digital shelves in a store environment which may be personalized with products and services of interest to a customer of the store.
Digital shelves are digital displays (similar to a computer screen) in a store where products and services may be displayed to customers within the store in place of, or in addition to, physical products on shelves. Present digital shelves are advantageous in that prices for products and services may be updated instantaneously. Moreover, promotions for products and services may be displayed to the customers and frequently updated to take into account market conditions.
However, digital shelves are generic and are not personalized for individuals or groups with similar interests. Further, digital shelves are static and are focused on displaying what is available in the store rather than displaying what is of interest to the customer.
In information technology, big data is a collection of data sets so large or complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, analysis, visualization and information privacy. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.
While on-line commerce is now well established, and big data is beginning to become an important factor in personalizing user experiences across a range of on-line activities, the brick and mortar world remains unaware of all user information except for, perhaps during the execution of sales transactions, when stored user profiles linked to the user's identity may be used for authentication and, perhaps, to offer point of sales incentives.
Big data size is a constantly moving target and presently may extend beyond many petabytes of data. Big data is a set of techniques and technologies that require new forms of integration to uncover large hidden values from large datasets that are diverse, complex, and of a massive scale.
Big data may be characterized by the four “V”s—volume, velocity, variety and veracity.
Volume (data at scale) refers to the quantity of data that is generated. It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered Big Data or not.
Velocity (data in motion) refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development.
Variety (data in many forms) refers to managing many types of data and understanding and analyzing them in their native form.
Veracity (trustworthiness of data) refers to the quality of the data being captured. The veracity of data can vary greatly. Accuracy of analysis depends on the veracity of the source data. Veracity is essential for decisions.